Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France.
Department of Radiological Sciences, University of California Irvine, Irvine, CA 92697, USA; Center for Artificial Intelligence in Diagnostic Medicine, University of California Irvine, Irvine, CA 92697, USA.
Clin Imaging. 2024 Sep;113:110245. doi: 10.1016/j.clinimag.2024.110245. Epub 2024 Jul 30.
Diagnosing pulmonary embolism (PE) is still challenging due to other conditions that can mimic its appearance, leading to incomplete or delayed management and several inter-observer variabilities. This study evaluated the performance and clinical utility of an artificial intelligence (AI)-based application designed to assist clinicians in the detection of PE on CT pulmonary angiography (CTPA).
CTPAs from 230 US cities acquired on 57 scanner models from 6 different vendors were retrospectively collected. Three US board certified expert radiologists defined the ground truth by majority agreement. The same cases were analyzed by CINA-PE, an AI-driven algorithm capable of detecting and highlighting suspected PE locations. The algorithm's performance at a per-case and per-finding level was evaluated. Furthermore, cases with PE not mentioned in the clinical report but correctly detected by the algorithm were analyzed.
A total of 1204 CTPAs (mean age 62.1 years ± 16.6[SD], 44.4 % female, 14.9 % positive) were included in the study. Per-case sensitivity and specificity were 93.9 % (95%CI: 89.3 %-96.9 %) and 94.8 % (95%CI: 93.3 %-96.1 %), respectively. Per-finding positive predictive value was 89.5 % (95%CI: 86.7 %-91.9 %). Among the 196 positive cases, 29 (15.6 %) were not mentioned in the clinical report. The algorithm detected 22/29 (76 %) of these cases, leading to a reduction in the miss rate from 15.6 % to 3.8 % (7/186).
The AI-based application may improve diagnostic accuracy in detecting PE and enhance patient outcomes through timely intervention. Integrating AI tools in clinical workflows can reduce missed or delayed diagnoses, and positively impact healthcare delivery and patient care.
由于其他可模拟肺栓塞（PE）表现的病症的存在，诊断 PE 仍然具有挑战性，这导致治疗不完整或延迟，以及存在多种观察者间差异。本研究评估了一种基于人工智能（AI）的应用程序在 CT 肺动脉造影（CTPA）中协助临床医生检测 PE 的性能和临床实用性。
回顾性收集了来自 6 个不同供应商的 57 种型号扫描仪的 230 个美国城市的 CTPA。3 位具有美国董事会认证的专家放射科医生通过多数协议定义了真实情况。相同的病例由 CINA-PE 进行分析，这是一种能够检测和突出可疑 PE 位置的 AI 驱动算法。评估了该算法在逐个病例和逐个发现水平上的性能。此外，还分析了临床报告中未提及但算法正确检测到的 PE 病例。
本研究共纳入 1204 例 CTPA（平均年龄 62.1±16.6[SD]岁，44.4%为女性，14.9%为阳性）。逐个病例的敏感性和特异性分别为 93.9%（95%CI：89.3%-96.9%）和 94.8%（95%CI：93.3%-96.1%）。逐个发现的阳性预测值为 89.5%（95%CI：86.7%-91.9%）。在 196 例阳性病例中，29 例（15.6%）在临床报告中未提及。该算法检测到 22/29（76%）例这些病例，使漏诊率从 15.6%降至 3.8%（7/186）。
基于 AI 的应用程序可以提高检测 PE 的诊断准确性，并通过及时干预改善患者的预后。将 AI 工具整合到临床工作流程中可以减少漏诊或延迟诊断的发生，并对医疗服务的提供和患者护理产生积极影响。
